class_docstr: 2D convolution layer (e.g. spatial convolution over images).
  
  This layer creates a convolution kernel that is convolved
  with the layer input to produce a tensor of
  outputs. If `use_bias` is True,
  a bias vector is created and added to the outputs. Finally, if
  `activation` is not `None`, it is applied to the outputs as well.
  
  When using this layer as the first layer in a model,
  provide the keyword argument `input_shape`
  (tuple of integers, does not include the sample axis),
  e.g. `input_shape=(128, 128, 3)` for 128x128 RGB pictures
  in `data_format="channels_last"`.
  
  # Arguments
      filters: Integer, the dimensionality of the output space
          (i.e. the number of output filters in the convolution).
      kernel_size: An integer or tuple/list of 2 integers, specifying the
          width and height of the 2D convolution window.
          Can be a single integer to specify the same value for
          all spatial dimensions.
      strides: An integer or tuple/list of 2 integers,
          specifying the strides of the convolution along the width and height.
          Can be a single integer to specify the same value for
          all spatial dimensions.
          Specifying any stride value != 1 is incompatible with specifying
          any `dilation_rate` value != 1.
      padding: one of `"valid"` or `"same"` (case-insensitive).
          Note that `"same"` is slightly inconsistent across backends with
          `strides` != 1.
      data_format: A string,
          one of `channels_last` (default) or `channels_first`.
          The ordering of the dimensions in the inputs.
          `channels_last` corresponds to inputs with shape
          `(batch, height, width, channels)` while `channels_first`
          corresponds to inputs with shape
          `(batch, channels, height, width)`.
          It defaults to the `image_data_format` value found in your
          Keras config file at `~/.keras/keras.json`.
          If you never set it, then it will be "channels_last".
      activation: Activation function to use.
          If you don't specify anything, no activation is applied
          (ie. "linear" activation: `a(x) = x`).
      use_bias: Boolean, whether the layer uses a bias vector.
      kernel_initializer: Initializer for the `kernel` weights matrix
      bias_initializer: Initializer for the bias vector
      kernel_regularizer: Regularizer function applied to
          the `kernel` weights matrix
      bias_regularizer: Regularizer function applied to the bias vector
  
  # Input shape
      4D tensor with shape:
      `(samples, channels, rows, cols)` if data_format='channels_first'
      or 4D tensor with shape:
      `(samples, rows, cols, channels)` if data_format='channels_last'.
  
  # Output shape
      4D tensor with shape:
      `(samples, filters, new_rows, new_cols)` if data_format='channels_first'
      or 4D tensor with shape:
      `(samples, new_rows, new_cols, filters)` if data_format='channels_last'.
      `rows` and `cols` values might have changed due to padding.
  
  >>> conv2d = Conv2D(12, kernel_size=(2, 5), input_shape=(3, 16, 16))
  creating: createZooKeras2Conv2D
code: def __init__(
      self,
      filters,
      kernel_size,
      strides=(1, 1),
      padding="valid",
      data_format="channels_first",
      activation=None,
      use_bias=True,
      kernel_initializer="glorot_uniform",
      bias_initializer="zero",
      kernel_regularizer=None,
      bias_regularizer=None,
      input_shape=None,
      **kwargs,
  ):
      super(Conv2D, self).__init__(
          None,
          filters,
          kernel_size,
          strides,
          padding,
          data_format,
          activation,
          use_bias,
          kernel_initializer,
          bias_initializer,
          kernel_regularizer,
          bias_regularizer,
          list(input_shape) if input_shape else None,
          **kwargs,
      )
  
file_path: analytics-zoo/pyzoo/zoo/pipeline/api/keras2/layers/convolutional.py
imports: import sys
  from zoo.pipeline.api.keras2.base import ZooKeras2Layer
label_desc: Use image node_id 1 to create a new Conv2D object from inherited base classes: ZooKeras2Layer with example: obj = Conv2D(filters, kernel_size, strides, padding, data_format, activation, use_bias, kernel_initializer, bias_initializer, kernel_regularizer, bias_regularizer, input_shape)